OpenRaiser/Intern-Atlas huggingface dataset:
A Systematic Approach to Incremental Redundancy over Erasure Channels
Venue: arXiv cs.IT 2018
Year: 2018

As sensing and instrumentation play an increasingly important role in systems controlled over wired and wireless networks, the need to better understand delay-sensitive communication becomes a prime issue. Along these lines, this article studies the operation of data links that employ incremental redundancy as a practical means to protect information from the effects of unreliable channels. Specifically, this work extends a powerful methodology termed sequential differential optimization to choose near-optimal block sizes for hybrid ARQ over erasure channels. In doing so, an interesting connection between random coding and well-known constants in number theory is established. Furthermore, results show that the impact of the coding strategy adopted and the propensity of the channel to erase symbols naturally decouple when analyzing throughput. Overall, block size selection is motivated by normal approximations on the probability of decoding success at every stage of the incremental transmission process. This novel perspective, which rigorously bridges hybrid ARQ and coding, offers a pragmatic means to select code rates and blocklengths for incremental redundancy.
Delay and Peak-Age Violation Probability in Short-Packet Transmissions
Venue: arXiv cs.IT 2018
Year: 2018

This paper investigates the distribution of delay and peak age of information in a communication system where packets, generated according to an independent and identically distributed Bernoulli process, are placed in a single-server queue with first-come first-served discipline and transmitted over an additive white Gaussian noise (AWGN) channel. When a packet is correctly decoded, the sender receives an instantaneous error-free positive acknowledgment, upon which it removes the packet from the buffer. In the case of negative acknowledgment, the packet is retransmitted. By leveraging finite-blocklength results for the AWGN channel, we characterize the delay violation and the peak-age violation probability without resorting to approximations based on large deviation theory as in previous literature. Our analysis reveals that there exists an optimum blocklength that minimizes the delay violation and the peak-age violation probabilities. We also show that one can find two blocklength values that result in very similar average delay but significantly different delay violation probabilities. This highlights the importance of focusing on violation probabilities rather than on averages.
Highly Scalable Image Reconstruction using Deep Neural Networks with Bandpass Filtering
Venue: arXiv cs.CV 2018
Year: 2018

To increase the flexibility and scalability of deep neural networks for image reconstruction, a framework is proposed based on bandpass filtering. For many applications, sensing measurements are performed indirectly. For example, in magnetic resonance imaging, data are sampled in the frequency domain. The introduction of bandpass filtering enables leveraging known imaging physics while ensuring that the final reconstruction is consistent with actual measurements to maintain reconstruction accuracy. We demonstrate this flexible architecture for reconstructing subsampled datasets of MRI scans. The resulting high subsampling rates increase the speed of MRI acquisitions and enable the visualization rapid hemodynamics.
Mining Top-k Sequential Patterns in Database Graphs:A New Challenging Problem and a Sampling-based Approach
Venue: arXiv cs.DB 2018
Year: 2018

In many real world networks, a vertex is usually associated with a transaction database that comprehensively describes the behaviour of the vertex. A typical example is the social network, where the behaviour of every user is depicted by a transaction database that stores his daily posted contents. A transaction database is a set of transactions, where a transaction is a set of items. Every path of the network is a sequence of vertices that induces multiple sequences of transactions. The sequences of transactions induced by all of the paths in the network forms an extremely large sequence database. Finding frequent sequential patterns from such sequence database discovers interesting subsequences that frequently appear in many paths of the network. However, it is a challenging task, since the sequence database induced by a database graph is too large to be explicitly induced and stored. In this paper, we propose the novel notion of database graph, which naturally models a wide spectrum of real world networks by associating each vertex with a transaction database. Our goal is to find the top-k frequent sequential patterns in the sequence database induced from a database graph. We prove that this problem is #P-hard. To tackle this problem, we propose an efficient two-step sampling algorithm that approximates the top-k frequent sequential patterns with provable quality guarantee. Extensive experimental results on synthetic and real-world data sets demonstrate the effectiveness and efficiency of our method.
Mining and Forecasting Career Trajectories of Music Artists
Venue: arXiv cs.SI 2018
Year: 2018

Many musicians, from up-and-comers to established artists, rely heavily on performing live to promote and disseminate their music. To advertise live shows, artists often use concert discovery platforms that make it easier for their fans to track tour dates. In this paper, we ask whether digital traces of live performances generated on those platforms can be used to understand career trajectories of artists. First, we present a new dataset we constructed by cross-referencing data from such platforms. We then demonstrate how this dataset can be used to mine and predict important career milestones for the musicians, such as signing by a major music label, or performing at a certain venue. Finally, we perform a temporal analysis of the bipartite artist-venue graph, and demonstrate that high centrality on this graph is correlated with success.
Character-level Chinese-English Translation through ASCII Encoding
Venue: arXiv cs.CL 2018
Year: 2018

Character-level Neural Machine Translation (NMT) models have recently achieved impressive results on many language pairs. They mainly do well for Indo-European language pairs, where the languages share the same writing system. However, for translating between Chinese and English, the gap between the two different writing systems poses a major challenge because of a lack of systematic correspondence between the individual linguistic units. In this paper, we enable character-level NMT for Chinese, by breaking down Chinese characters into linguistic units similar to that of Indo-European languages. We use the Wubi encoding scheme, which preserves the original shape and semantic information of the characters, while also being reversible. We show promising results from training Wubi-based models on the character- and subword-level with recurrent as well as convolutional models.
A Proposal for Loop-Transformation Pragmas
Venue: arXiv cs.PL 2018
Year: 2018

Pragmas for loop transformations, such as unrolling, are implemented in most mainstream compilers. They are used by application programmers because of their ease of use compared to directly modifying the source code of the relevant loops. We propose additional pragmas for common loop transformations that go far beyond the transformations today's compilers provide and should make most source rewriting for the sake of loop optimization unnecessary. To encourage compilers to implement these pragmas, and to avoid a diversity of incompatible syntaxes, we would like to spark a discussion about an inclusion to the OpenMP standard.
Computing an LLL-reduced basis of the orthogonal lattice
Venue: arXiv cs.SC 2018
Year: 2018

As a typical application, the Lenstra-Lenstra-Lovasz lattice basis reduction algorithm (LLL) is used to compute a reduced basis of the orthogonal lattice for a given integer matrix, via reducing a special kind of lattice bases. With such bases in input, we propose a new technique for bounding from above the number of iterations required by the LLL algorithm. The main technical ingredient is a variant of the classical LLL potential, which could prove useful to understand the behavior of LLL for other families of input bases.
Controlling the privacy loss with the input feature maps of the layers in convolutional neural networks
Venue: arXiv cs.LG 2018
Year: 2018

We propose the method to sanitize the privacy of the IFM(Input Feature Map)s that are fed into the layers of CNN(Convolutional Neural Network)s. The method introduces the degree of the sanitization that makes the application using a CNN be able to control the privacy loss represented as the ratio of the probabilistic accuracies for original IFM and sanitized IFM. For the sanitization of an IFM, the sample-and-hold based approximation scheme is devised to satisfy an application-specific degree of the sanitization. The scheme approximates an IFM by replacing all the samples in a window with the non-zero sample closest to the mean of the sampling window. It also removes the dependency on CNN configuration by unfolding multi-dimensional IFM tensors into one-dimensional streams to be approximated.
Object Tracking with Correlation Filters using Selective Single Background Patch
Venue: arXiv cs.CV 2018
Year: 2018

Correlation filter plays a major role in improved tracking performance compared to existing trackers. The tracker uses the adaptive correlation response to predict the location of the target. Many varieties of correlation trackers were proposed recently with high accuracy and frame rates. The paper proposes a method to select a single background patch to have a better tracking performance. The paper also contributes a variant of correlation filter by modifying the filter with image restoration filters. The approach is validated using Object Tracking Benchmark sequences.
Assessing Security and Performances of Consensus algorithms for Permissioned Blockchains
Venue: arXiv cs.CR 2018
Year: 2018

Blockchain is a novel technology that is rising a lot of interest in the industrial and re- search sectors because its properties of decentralisation, immutability and data integrity. Initially, the underlying consensus mechanism has been designed for permissionless block- chain on trustless network model through the proof-of-work, i.e. a mathematical challenge which requires high computational power. This solution suffers of poor performances, hence alternative consensus algorithms as the proof-of-stake have been proposed. Conversely, for permissioned blockchain, where participants are known and authenti- cated, variants of distributed consensus algorithms have been employed. However, most of them comes out without formal expression of security analysis and trust assumptions because the absence of an established knowledge. Therefore the lack of adequate analysis on these algorithms hinders any cautious evaluation of their effectiveness in a real-world setting where systems are deployed over trustless networks, i.e. Internet ...
Monitoring Dynamic Mobile Ad-Hoc Networks: A Fully Distributed Hybrid Architecture
Venue: arXiv cs.NI 2018
Year: 2018

The mobile ad-hoc networks (MANETs) represent a broad area of study and market interest. They provide a wide set of applications in multiple domains. In that context, the functional and non-functional monitoring of these networks is crucial. For that purpose, monitoring techniques have been deeply studied in wired networks using gossip-based or hierarchical-based approaches. However, when applied to a MANET, several problematics arise mainly due to the absence of a centralized administration, the inherent MANETs constraints and the nodes mobility. In this paper, we present a hybrid distributed monitoring architecture for mobile adhoc networks in context of mobility pattern. We get inspired of gossip-based and hierarchical-based algorithms for query dissemination and data aggregation. We define gossip-based mechanisms that help our virtual hierarchical topology to complete the data aggregation, and then ensure the stability and robustness of our approach in dynamic environments. Further, we propose a fully distributed monitoring protocol that ease the nodes communications. We evaluate our approach through a simulated testbed by using NS3 and Docker, and illustrate the efficiency of our mechanisms.
Ten simple rules for measuring the impact of workshops
Venue: arXiv cs.CY 2018
Year: 2018

Workshops are used to explore a specific topic, transfer knowledge, solve identified problems or create something new. In funded research projects and other research endeavours, workshops are the mechanism to gather the wider project, community or interested people together around a particular topic. However, natural questions arise: how do we measure the impact of these workshops? Do we know whether they are meeting the goals and objectives we set for them? What indicators should we use? In response to these questions, this paper will outline rules that will improve the measurement of the impact of workshops.
Multicast Networks Solvable over Every Finite Field
Venue: arXiv cs.IT 2018
Year: 2018

In this work, it is revealed that an acyclic multicast network that is scalar linearly solvable over Galois Field of two elements, GF(2), is solvable over all higher finite fields. An algorithm which, given a GF(2) solution for an acyclic multicast network, computes the solution over any arbitrary finite field is presented. The concept of multicast matroid is introduced in this paper. Gammoids and their base-orderability along with the regularity of a binary multicast matroid are used to prove the results.
End-to-End Polyphonic Sound Event Detection Using Convolutional Recurrent Neural Networks with Learned Time-Frequency Representation Input
Venue: arXiv cs.SD 2018
Year: 2018

Sound event detection systems typically consist of two stages: extracting hand-crafted features from the raw audio waveform, and learning a mapping between these features and the target sound events using a classifier. Recently, the focus of sound event detection research has been mostly shifted to the latter stage using standard features such as mel spectrogram as the input for classifiers such as deep neural networks. In this work, we utilize end-to-end approach and propose to combine these two stages in a single deep neural network classifier. The feature extraction over the raw waveform is conducted by a feedforward layer block, whose parameters are initialized to extract the time-frequency representations. The feature extraction parameters are updated during training, resulting with a representation that is optimized for the specific task. This feature extraction block is followed by (and jointly trained with) a convolutional recurrent network, which has recently given state-of-the-art results in many sound recognition tasks. The proposed system does not outperform a convolutional recurrent network with fixed hand-crafted features. The final magnitude spectrum characteristics of the feature extraction block parameters indicate that the most relevant information for the given task is contained in 0 - 3 kHz frequency range, and this is also supported by the empirical results on the SED performance.
Decentralized Collaborative Knowledge Management using Git
Venue: arXiv cs.DB 2018
Year: 2018

The World Wide Web and the Semantic Web are designed as a network of distributed services and datasets. The distributed character of the Web brings manifold collaborative possibilities to interchange data. The commonly adopted collaborative solutions for RDF data are centralized (e.g. SPARQL endpoints and wiki systems). But to support distributed collaboration, a system is needed, that supports divergence of datasets, brings the possibility to conflate diverged states, and allows distributed datasets to be synchronized. In this paper, we present Quit Store, it was inspired by and it builds upon the successful Git system. The approach is based on a formal expression of evolution and consolidation of distributed datasets. During the collaborative curation process, the system automatically versions the RDF dataset and tracks provenance information. It also provides support to branch, merge, and synchronize distributed RDF datasets. The merging process is guarded by specific merge strategies for RDF data. Finally, we use our reference implementation to show overall good performance and demonstrate the practical usability of the system.
EPA-RIMM: A Framework for Dynamic SMM-based Runtime Integrity Measurement
Venue: arXiv cs.CR 2018
Year: 2018

Runtime integrity measurements identify unexpected changes in operating systems and hypervisors during operation, enabling early detection of persistent threats. System Management Mode, a privileged x86 CPU mode, has the potential to effectively perform such rootkit detection. Previously proposed SMM-based approaches demonstrated effective detection capabilities, but at a cost of performance degradation and software side effects. In this paper we introduce our solution to these problems, an SMM-based Extensible, Performance Aware Runtime Integrity Measurement Mechanism called EPA-RIMM. The EPA-RIMM architecture features a performance-sensitive design that decomposes large integrity measurements and schedules them to control perturbation and side effects. EPA-RIMM's decomposition of long-running measurements into shorter tasks, extensibility, and use of SMM complicates the efforts of malicious code to detect or avoid the integrity measurements. Using a Minnowboard-based prototype, we demonstrate its detection capabilities and performance impacts. Early results are promising, and suggest that EPA-RIMM will meet production-level performance constraints while continuously monitoring key OS and hypervisor data structures for signs of attack.
UAV-Enabled Wireless Power Transfer with Directional Antenna: A Two-User Case
Venue: arXiv cs.IT 2018
Year: 2018

This paper considers an unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) system, in which a UAV equipped with a directional antenna is dispatched to deliver wireless energy to charge two energy receivers (ERs) on the ground. Under this setup, we maximize the common (or minimum) energy received by the two ERs over a particular finite charging period, by jointly optimizing the altitude, trajectory, and transmit beamwidth of the UAV, subject to the UAV's maximum speed constraints, as well as the maximum/minimum altitude and beamwidth constraints. However, the common energy maximization is a non-convex optimization problem that is generally difficult to be solved optimally. To tackle this problem, we first ignore the maximum UAV speed constraints and solve the relaxed problem optimally. The optimal solution to the relaxed problem reveals that the UAV should hover above two symmetric locations during the whole charging period, with the corresponding altitude and beamwidth optimized. Next, we study the original problem with the maximum UAV speed constraints considered, for which a heuristic hover-fly-hover trajectory design is proposed based on the optimal symmetric-location-hovering solution to the relaxed problem. Numerical results validate that thanks to the employment of directional antenna with adaptive beamwidth and altitude control, our proposed design significantly improves the common energy received by the two ERs, as compared to other benchmark schemes.
OK Google, What Is Your Ontology? Or: Exploring Freebase Classification to Understand Google's Knowledge Graph
Venue: arXiv cs.IR 2018
Year: 2018

This paper reconstructs the Freebase data dumps to understand the underlying ontology behind Google's semantic search feature. The Freebase knowledge base was a major Semantic Web and linked data technology that was acquired by Google in 2010 to support the Google Knowledge Graph, the backend for Google search results that include structured answers to queries instead of a series of links to external resources. After its shutdown in 2016, Freebase is contained in a data dump of 1.9 billion Resource Description Format (RDF) triples. A recomposition of the Freebase ontology will be analyzed in relation to concepts and insights from the literature on classification by Bowker and Star. This paper will explore how the Freebase ontology is shaped by many of the forces that also shape classification systems through a deep dive into the ontology and a small correlational study. These findings will provide a glimpse into the proprietary blackbox Knowledge Graph and what is meant by Google's mission to "organize the world's information and make it universally accessible and useful".
Low Rank Tensor Completion for Multiway Visual Data
Venue: arXiv cs.NA 2018
Year: 2018

Tensor completion recovers missing entries of multiway data. Teh missing of entries could often be caused during teh data acquisition and transformation. In dis paper, we provide an overview of recent development in low rank tensor completion for estimating teh missing components of visual data, e. g. , color images and videos. First, we categorize these methods into two groups based on teh different optimization models. One optimizes factors of tensor decompositions wif predefined tensor rank. Teh other iteratively updates teh estimated tensor via minimizing teh tensor rank. Besides, we summarize teh corresponding algorithms to solve those optimization problems in details. Numerical experiments are given to demonstrate teh performance comparison when different methods are applied to color image and video processing.
SWIPT Signalling over Complex AWGN Channels with Two Nonlinear Energy Harvester Models
Venue: arXiv cs.IT 2018
Year: 2018

Simultaneous Wireless Information and Power Transfer (SWIPT) is subject to nonlinearity at the energy harvester that leads to significant changes to transmit signal designs compared to conventional wireless communications. In this paper, the capacity of a discrete time, memoryless and complex Additive White Gaussian Noise (AWGN) channel in the presence of a nonlinear energy harvester at the receiver is studied. Considering the two common nonlinear energy harvester models introduced in the literature, two sets of constraints are considered. First the capacity is studied under average power (AP), peak amplitude (PA) and receiver delivery power (RDP) constraints. The RDP constraint is modelled as a linear combination of even-moment statistics of the channel input being larger than a threshold. It is shown that the capacity of an AWGN channel under AP and RDP constraints is the same as the capacity of an AWGN channel under an AP constraint, however, depending on the two constraints, it can be either achieved or arbitrarily approached. It is also shown that under AP, PA and RDP constraints, the amplitude of the optimal inputs is discrete with a finite number of mass points. Next, the capacity is studied under AP, PA and output outage probability (OOP) constraints. OOP is modelled as satisfying a certain probability inequality for the amplitude of the received signal being outside of a given interval. Similarly, it is shown that the amplitude of the optimal input is discrete with a finite number of mass points.